Thesis: Genome-scale constraint-based in silico modelling of Arabidopsis thaliana energy metabolism.
Recent advances in sequencing technology have enabled the elucidation of entire genome sequences for a number of key organisms. Laboratory experiments and bioinformatics analysis of these sequences should reveal the complete set of molecular components involved in cellular biochemical activities. The next challenge will be to reconstruct and simulate cellular functions based on this data.
This project will attempt the reconstruction of a comprehensive genome-scale model of Arabidopsis thaliana metabolism, with a focus on energy metabolism. This computational model will be used to perform simulations of the energy metabolism of plant cells, under chosen constraints.
Why my research is important
The huge amount of energy generated by plants though photosynthesis and respiration is essential on our planet. Life would not be possible without the oxygen and the food produced by plants. Despite this fact, we still do not know all the mechanisms that make plants such good natural power stations.
This project will allow us to gain a better insight into plant energy metabolism and how it gets altered with changes in environmental conditions. We expect that this approach will generate new discoveries and knowledge for improving plant performance, particularly in marginal environments and in response to climate change. It is also intended to use the model for metabolic engineering purposes, predicting the necessary changes to achieve desired outcomes in biotechnology or agriculture, for example, to enhance the yield and nutritional value of a range of agricultural products.